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Bioinformatics. 2019 Feb 15;35(4):656-664. doi: 10.1093/bioinformatics/bty646.

Automated workflow composition in mass spectrometry-based proteomics.

Author information

1
Center for Proteomics and Metabolomics, Leiden University Medical Center, RC Leiden, The Netherlands.
2
Department of Information and Computing Sciences, Utrecht University, CC Utrecht, The Netherlands.
3
National Life Science Supercomputing Center, Technical University of Denmark, Kongens Lyngby, Denmark.
4
Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark.

Abstract

MOTIVATION:

Numerous software utilities operating on mass spectrometry (MS) data are described in the literature and provide specific operations as building blocks for the assembly of on-purpose workflows. Working out which tools and combinations are applicable or optimal in practice is often hard. Thus researchers face difficulties in selecting practical and effective data analysis pipelines for a specific experimental design.

RESULTS:

We provide a toolkit to support researchers in identifying, comparing and benchmarking multiple workflows from individual bioinformatics tools. Automated workflow composition is enabled by the tools' semantic annotation in terms of the EDAM ontology. To demonstrate the practical use of our framework, we created and evaluated a number of logically and semantically equivalent workflows for four use cases representing frequent tasks in MS-based proteomics. Indeed we found that the results computed by the workflows could vary considerably, emphasizing the benefits of a framework that facilitates their systematic exploration.

AVAILABILITY AND IMPLEMENTATION:

The project files and workflows are available from https://github.com/bio-tools/biotoolsCompose/tree/master/Automatic-Workflow-Composition.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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